MARS fits a nonlinear function to the observed data that is simple enough to be
interpretable because it is built from the basic ReLU functions \((x_j- t_j)_+\)  and \((t_j - x_j)_+\) . Further, MARS fits non-additive
functions because of the presence of products in (REF ), which enables interactions between the explanatory variables \(x_1,\dots , x_d\) . The exact methodology that MARS uses involves a greedy
algorithm similar to stepwise regression methods. Specifically, one
adds in basis functions of the form (REF ) starting with a
constant function using a goodness of fit criterion. Once a
reasonably large number of basis functions are added, a backward
deletion procedure is applied in order to remove superfluous basis
functions. We refer the reader to [1]} for details.
